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LWT - Food Science and Technology
Vol. 62 (2), 2015, Pages: 10601068

Rapid and non-invasive detection of fish microbial spoilage by visible and near infrared hyperspectral imaging and multivariate analysis

Jun-Hu Chenga, Da-Wen Sun

College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China.


The feasibility of visible and near infrared hyperspectral imaging in the range of 400–1000 nm for determinating total viable counts (TVC) to evaluate microbial spoilage of fish fillets was investigated. Partial least square regression (PLSR) and least square support vector machines (LS-SVM) models established based on full wavelengths showed excellent performances and the LS-SVM model was better with higher residual predictive deviation (RPD) of 3.89, determination coefficients in prediction (PR2) of 0.93 and lower root mean square errors in prediction (RMSEP) of 0.49 log10 CFU/g. Seven optimal wavelengths were selected by successive projections algorithm (SPA) and the simplified SPA-PLSR was better than SPA-LS-SVM models with RPD of 3.13, PR2 of 0.90 and RMSEP of 0.57 log10 CFU/g, and was transferred to each pixel of the hyperspectral images for generating the TVC distribution map. This study showed that hyperspectral imaging is suitable to determine TVC value for evaluating microbial spoilage of grass carp fillets in a rapid and non-invasive manner.

Keywords: Hyperspectral imaging; Total viable counts (TVC); Successive projections algorithm (SPA); Grass carp fillet; Prediction map.

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